Translations:Softmax Function/14/en: Difference between revisions

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    * <math>T \to 0</math>: The distribution collapses to a one-hot vector selecting the argmax — equivalent to a hard decision.
    * <math>T \to 0</math>: The distribution collapses to a {{Term|one-hot encoding|one-hot}} vector selecting the argmax — equivalent to a hard decision.
    * <math>T = 1</math>: Standard softmax.
    * <math>T = 1</math>: Standard softmax.
    * <math>T \to \infty</math>: The distribution approaches uniform — all classes become equally likely.
    * <math>T \to \infty</math>: The distribution approaches uniform — all classes become equally likely.

    Latest revision as of 23:34, 27 April 2026

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    Message definition (Softmax Function)
    * <math>T \to 0</math>: The distribution collapses to a {{Term|one-hot encoding|one-hot}} vector selecting the argmax — equivalent to a hard decision.
    * <math>T = 1</math>: Standard softmax.
    * <math>T \to \infty</math>: The distribution approaches uniform — all classes become equally likely.
    • $ T \to 0 $: The distribution collapses to a one-hot vector selecting the argmax — equivalent to a hard decision.
    • $ T = 1 $: Standard softmax.
    • $ T \to \infty $: The distribution approaches uniform — all classes become equally likely.
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